Prediction of SARS-CoV-2 infection with a Symptoms-Based model to aid public health decision making in Latin America and other low and middle income settings
Symptoms-based models for predicting SARS-CoV-2 infection may improve clinical decision-making and be an alternative to resource allocation in under-resourced settings. In this study we aimed to test a model based on symptoms to predict a positive test result for SARS-CoV-2 infection during the COVI...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Preventive Medicine Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S221133552200105X |
| _version_ | 1828730762686365696 |
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| author | Andrea Ramírez Varela Sergio Moreno López Sandra Contreras-Arrieta Guillermo Tamayo-Cabeza Silvia Restrepo-Restrepo Ignacio Sarmiento-Barbieri Yuldor Caballero-Díaz Luis Jorge Hernandez-Florez John Mario González Leonardo Salas-Zapata Rachid Laajaj Giancarlo Buitrago-Gutierrez Fernando de la Hoz-Restrepo Martha Vives Florez Elkin Osorio Diana Sofía Ríos-Oliveros Eduardo Behrentz |
| author_facet | Andrea Ramírez Varela Sergio Moreno López Sandra Contreras-Arrieta Guillermo Tamayo-Cabeza Silvia Restrepo-Restrepo Ignacio Sarmiento-Barbieri Yuldor Caballero-Díaz Luis Jorge Hernandez-Florez John Mario González Leonardo Salas-Zapata Rachid Laajaj Giancarlo Buitrago-Gutierrez Fernando de la Hoz-Restrepo Martha Vives Florez Elkin Osorio Diana Sofía Ríos-Oliveros Eduardo Behrentz |
| author_sort | Andrea Ramírez Varela |
| collection | DOAJ |
| description | Symptoms-based models for predicting SARS-CoV-2 infection may improve clinical decision-making and be an alternative to resource allocation in under-resourced settings. In this study we aimed to test a model based on symptoms to predict a positive test result for SARS-CoV-2 infection during the COVID-19 pandemic using logistic regression and a machine-learning approach, in Bogotá, Colombia. Participants from the CoVIDA project were included. A logistic regression using the model was chosen based on biological plausibility and the Akaike Information criterion. Also, we performed an analysis using machine learning with random forest, support vector machine, and extreme gradient boosting. The study included 58,577 participants with a positivity rate of 5.7%. The logistic regression showed that anosmia (aOR = 7.76, 95% CI [6.19, 9.73]), fever (aOR = 4.29, 95% CI [3.07, 6.02]), headache (aOR = 3.29, 95% CI [1.78, 6.07]), dry cough (aOR = 2.96, 95% CI [2.44, 3.58]), and fatigue (aOR = 1.93, 95% CI [1.57, 2.93]) were independently associated with SARS-CoV-2 infection. Our final model had an area under the curve of 0.73. The symptoms-based model correctly identified over 85% of participants. This model can be used to prioritize resource allocation related to COVID-19 diagnosis, to decide on early isolation, and contact-tracing strategies in individuals with a high probability of infection before receiving a confirmatory test result. This strategy has public health and clinical decision-making significance in low- and middle-income settings like Latin America. |
| first_indexed | 2024-04-12T17:35:56Z |
| format | Article |
| id | doaj.art-8348b7e7f6384cc1bd197c7ddfa68ae5 |
| institution | Directory Open Access Journal |
| issn | 2211-3355 |
| language | English |
| last_indexed | 2024-04-12T17:35:56Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Preventive Medicine Reports |
| spelling | doaj.art-8348b7e7f6384cc1bd197c7ddfa68ae52022-12-22T03:22:59ZengElsevierPreventive Medicine Reports2211-33552022-06-0127101798Prediction of SARS-CoV-2 infection with a Symptoms-Based model to aid public health decision making in Latin America and other low and middle income settingsAndrea Ramírez Varela0Sergio Moreno López1Sandra Contreras-Arrieta2Guillermo Tamayo-Cabeza3Silvia Restrepo-Restrepo4Ignacio Sarmiento-Barbieri5Yuldor Caballero-Díaz6Luis Jorge Hernandez-Florez7John Mario González8Leonardo Salas-Zapata9Rachid Laajaj10Giancarlo Buitrago-Gutierrez11Fernando de la Hoz-Restrepo12Martha Vives Florez13Elkin Osorio14Diana Sofía Ríos-Oliveros15Eduardo Behrentz16Universidad de los Andes, Bogotá, Colombia; Corresponding author at: School of Medicine, Universidad de los Andes, Cra 7 #116-05, 110111 Bogotá, Colombia.Universidad de los Andes, Bogotá, ColombiaUniversidad de los Andes, Bogotá, ColombiaUniversidad de los Andes, Bogotá, ColombiaUniversidad de los Andes, Bogotá, ColombiaUniversidad de los Andes, Bogotá, ColombiaUniversidad de los Andes, Bogotá, ColombiaUniversidad de los Andes, Bogotá, ColombiaUniversidad de los Andes, Bogotá, ColombiaSecretaría Distrital de Salud de Bogotá, Bogotá, ColombiaUniversidad de los Andes, Bogotá, ColombiaInstituto de Investigaciones Clínicas, Universidad Nacional de Colombia. Bogotá, ColombiaUniversidad Nacional de Colombia, Bogotá, ColombiaUniversidad de los Andes, Bogotá, ColombiaSecretaría Distrital de Salud de Bogotá, Bogotá, ColombiaSecretaría Distrital de Salud de Bogotá, Bogotá, ColombiaUniversidad de los Andes, Bogotá, ColombiaSymptoms-based models for predicting SARS-CoV-2 infection may improve clinical decision-making and be an alternative to resource allocation in under-resourced settings. In this study we aimed to test a model based on symptoms to predict a positive test result for SARS-CoV-2 infection during the COVID-19 pandemic using logistic regression and a machine-learning approach, in Bogotá, Colombia. Participants from the CoVIDA project were included. A logistic regression using the model was chosen based on biological plausibility and the Akaike Information criterion. Also, we performed an analysis using machine learning with random forest, support vector machine, and extreme gradient boosting. The study included 58,577 participants with a positivity rate of 5.7%. The logistic regression showed that anosmia (aOR = 7.76, 95% CI [6.19, 9.73]), fever (aOR = 4.29, 95% CI [3.07, 6.02]), headache (aOR = 3.29, 95% CI [1.78, 6.07]), dry cough (aOR = 2.96, 95% CI [2.44, 3.58]), and fatigue (aOR = 1.93, 95% CI [1.57, 2.93]) were independently associated with SARS-CoV-2 infection. Our final model had an area under the curve of 0.73. The symptoms-based model correctly identified over 85% of participants. This model can be used to prioritize resource allocation related to COVID-19 diagnosis, to decide on early isolation, and contact-tracing strategies in individuals with a high probability of infection before receiving a confirmatory test result. This strategy has public health and clinical decision-making significance in low- and middle-income settings like Latin America.http://www.sciencedirect.com/science/article/pii/S221133552200105XSARS-CoV-2COVID-19Logistic modelMachine learningSymptomsAnosmia |
| spellingShingle | Andrea Ramírez Varela Sergio Moreno López Sandra Contreras-Arrieta Guillermo Tamayo-Cabeza Silvia Restrepo-Restrepo Ignacio Sarmiento-Barbieri Yuldor Caballero-Díaz Luis Jorge Hernandez-Florez John Mario González Leonardo Salas-Zapata Rachid Laajaj Giancarlo Buitrago-Gutierrez Fernando de la Hoz-Restrepo Martha Vives Florez Elkin Osorio Diana Sofía Ríos-Oliveros Eduardo Behrentz Prediction of SARS-CoV-2 infection with a Symptoms-Based model to aid public health decision making in Latin America and other low and middle income settings Preventive Medicine Reports SARS-CoV-2 COVID-19 Logistic model Machine learning Symptoms Anosmia |
| title | Prediction of SARS-CoV-2 infection with a Symptoms-Based model to aid public health decision making in Latin America and other low and middle income settings |
| title_full | Prediction of SARS-CoV-2 infection with a Symptoms-Based model to aid public health decision making in Latin America and other low and middle income settings |
| title_fullStr | Prediction of SARS-CoV-2 infection with a Symptoms-Based model to aid public health decision making in Latin America and other low and middle income settings |
| title_full_unstemmed | Prediction of SARS-CoV-2 infection with a Symptoms-Based model to aid public health decision making in Latin America and other low and middle income settings |
| title_short | Prediction of SARS-CoV-2 infection with a Symptoms-Based model to aid public health decision making in Latin America and other low and middle income settings |
| title_sort | prediction of sars cov 2 infection with a symptoms based model to aid public health decision making in latin america and other low and middle income settings |
| topic | SARS-CoV-2 COVID-19 Logistic model Machine learning Symptoms Anosmia |
| url | http://www.sciencedirect.com/science/article/pii/S221133552200105X |
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